Pub Date : 2021-10-23DOI: 10.1109/NILES53778.2021.9600522
B. A. Mohamed, Lamees N. Mahmoud, W. Al-Atabany, N. Salem
The precise detection and segmentation of cells in microscopic image sequences is an essential task in biomedical research, such as drug discovery and studying the development of tissues, organs, or entire organisms. However, the detection and segmentation of cells in phase contrast images with a halo and shade-off effects is still challenging. Lately, Mask Regional Convolutional Neural Network (Mask R-CNN) has been introduced for object detection and instance segmentation of natural images. This study investigates the efficacy of the Mask R-CNN to instantly detect and segment label-free microscopic images. The dataset used in this paper is taken from the ISBI cell tracking challenge. The Mask R-CNN is trained using different hyperparameters and compared to the U-Net model. Experimental results show that the Mask R-CNN model achieves 91.6 % when using ResNet-50 backbone and COCO weights.
{"title":"Instance Segmentation of 2D Label-Free Microscopic Images using Deep Learning","authors":"B. A. Mohamed, Lamees N. Mahmoud, W. Al-Atabany, N. Salem","doi":"10.1109/NILES53778.2021.9600522","DOIUrl":"https://doi.org/10.1109/NILES53778.2021.9600522","url":null,"abstract":"The precise detection and segmentation of cells in microscopic image sequences is an essential task in biomedical research, such as drug discovery and studying the development of tissues, organs, or entire organisms. However, the detection and segmentation of cells in phase contrast images with a halo and shade-off effects is still challenging. Lately, Mask Regional Convolutional Neural Network (Mask R-CNN) has been introduced for object detection and instance segmentation of natural images. This study investigates the efficacy of the Mask R-CNN to instantly detect and segment label-free microscopic images. The dataset used in this paper is taken from the ISBI cell tracking challenge. The Mask R-CNN is trained using different hyperparameters and compared to the U-Net model. Experimental results show that the Mask R-CNN model achieves 91.6 % when using ResNet-50 backbone and COCO weights.","PeriodicalId":249153,"journal":{"name":"2021 3rd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131104203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-23DOI: 10.1109/NILES53778.2021.9600492
Soha Ahmed Ehssan Aly, Aya Hassanin, Saddam Bekhet
Sign languages is a critical requirement that helps deaf people to express their needs, feelings and emotions using a variety of hand gestures throughout their daily life. This language had evolved in parallel with spoken languages, however, it do not resemble its counterparts in the same way. Moreover, it is as complex as any other spoken language, as each sign language embodies hundreds of signs, that differs from the next by slight changes in hand shape, position, motion direction, face and body parts contributing to each sign. Unfortunately, sign languages are not globally standardized, where the language differs between countries and has its own vocabulary and varies although they might look similar. Furthermore, publicly available datasets are limited in quality and most of the available translation services are expensive, due to the required skilled human personnel. This paper proposes a deep learning approach for sign language detection that is finely tailored for the Egyptian sign language (special case of the generic sign language). The model is built to harnesses the power of convolutional and recurrent networks by integrating them together to better recognize the sign language spatio-temporal data-feed. In addition, the paper proposes the first Egyptian sign language dataset for emotion words and pronouns. The experimental results demonstrated the proposed approach promising results on the introduced dataset using combined CNN with RNN models.
{"title":"ESLDL: An Integrated Deep Learning Model for Egyptian Sign Language Recognition","authors":"Soha Ahmed Ehssan Aly, Aya Hassanin, Saddam Bekhet","doi":"10.1109/NILES53778.2021.9600492","DOIUrl":"https://doi.org/10.1109/NILES53778.2021.9600492","url":null,"abstract":"Sign languages is a critical requirement that helps deaf people to express their needs, feelings and emotions using a variety of hand gestures throughout their daily life. This language had evolved in parallel with spoken languages, however, it do not resemble its counterparts in the same way. Moreover, it is as complex as any other spoken language, as each sign language embodies hundreds of signs, that differs from the next by slight changes in hand shape, position, motion direction, face and body parts contributing to each sign. Unfortunately, sign languages are not globally standardized, where the language differs between countries and has its own vocabulary and varies although they might look similar. Furthermore, publicly available datasets are limited in quality and most of the available translation services are expensive, due to the required skilled human personnel. This paper proposes a deep learning approach for sign language detection that is finely tailored for the Egyptian sign language (special case of the generic sign language). The model is built to harnesses the power of convolutional and recurrent networks by integrating them together to better recognize the sign language spatio-temporal data-feed. In addition, the paper proposes the first Egyptian sign language dataset for emotion words and pronouns. The experimental results demonstrated the proposed approach promising results on the introduced dataset using combined CNN with RNN models.","PeriodicalId":249153,"journal":{"name":"2021 3rd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130576161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-23DOI: 10.1109/NILES53778.2021.9600485
Mostafa AboelMaged, M. Mashaly, M. A. E. Ghany
The evolution of computer systems and application-specific integrated circuits led to an increase in their complexity. Consequently, verification is a vital procedure in the design process to ensure correct functionality of the designs. However, the increase in the design's complexity led to the increase in the cost and time needed for the verification in the design process. Thus, to decrease the verification process time and cost, and achieve the best coverage for the design under test; machine learning techniques are used. In this paper, a verification environment that utilizes constrained random verification technique is introduced. The environment uses dynamic reseeding and rewinding techniques. The environment is also integrated with machine learning algorithms as well to update the constraint at run time to speed up the time needed to reach full design coverage. The environment can utilize previous simulations data or prior knowledge of the design to train the model. The environment uses a different neural network topology than the state of the art. The proposed environment recorded a decrease of 83.5% in the time needed and about 60000 times decrease in the error rate for training the machine learning algorithm in comparison with the state of the art.
{"title":"Online Constraints Update Using Machine Learning for Accelerating Hardware Verification","authors":"Mostafa AboelMaged, M. Mashaly, M. A. E. Ghany","doi":"10.1109/NILES53778.2021.9600485","DOIUrl":"https://doi.org/10.1109/NILES53778.2021.9600485","url":null,"abstract":"The evolution of computer systems and application-specific integrated circuits led to an increase in their complexity. Consequently, verification is a vital procedure in the design process to ensure correct functionality of the designs. However, the increase in the design's complexity led to the increase in the cost and time needed for the verification in the design process. Thus, to decrease the verification process time and cost, and achieve the best coverage for the design under test; machine learning techniques are used. In this paper, a verification environment that utilizes constrained random verification technique is introduced. The environment uses dynamic reseeding and rewinding techniques. The environment is also integrated with machine learning algorithms as well to update the constraint at run time to speed up the time needed to reach full design coverage. The environment can utilize previous simulations data or prior knowledge of the design to train the model. The environment uses a different neural network topology than the state of the art. The proposed environment recorded a decrease of 83.5% in the time needed and about 60000 times decrease in the error rate for training the machine learning algorithm in comparison with the state of the art.","PeriodicalId":249153,"journal":{"name":"2021 3rd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128305810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Diabetes is a common, metabolic disease, that results in a high level of blood sugar. Patients diagnosed with diabetes suffer from a body that cannot effectively use the insulin or cannot produce a sufficient amount of insulin. Providing a method of detection via symptoms that can be noticed by the patient can prompt the patient to seek medical assistance more promptly and in turn to be correctly diagnosed and treated. This paper proposed a solution for the problem using machine learning techniques. We applied eight algorithms on a data set of 521 subjects. The results are compared to each other to find the best algorithm for this task. The algorithms used are from different families which are logistic regression, support vector machines-linear and nonlinear kernel, random forest, decision tree, adaptive boosting classifier, K-nearest neighbor, and naïve bayes. The results show a clear advantage of using Random Forest with an accuracy of 98% having used 80% of the dataset for training and 20% for testing.
{"title":"Diabetes Prediction Using Machine Learning: A Comparative Study","authors":"Mohamed Rady, Kareem Moussa, Mahmoud Mostafa, Abdelrahman Elbasry, Zeyad Ezzat, Walaa Medhat","doi":"10.1109/NILES53778.2021.9600091","DOIUrl":"https://doi.org/10.1109/NILES53778.2021.9600091","url":null,"abstract":"Diabetes is a common, metabolic disease, that results in a high level of blood sugar. Patients diagnosed with diabetes suffer from a body that cannot effectively use the insulin or cannot produce a sufficient amount of insulin. Providing a method of detection via symptoms that can be noticed by the patient can prompt the patient to seek medical assistance more promptly and in turn to be correctly diagnosed and treated. This paper proposed a solution for the problem using machine learning techniques. We applied eight algorithms on a data set of 521 subjects. The results are compared to each other to find the best algorithm for this task. The algorithms used are from different families which are logistic regression, support vector machines-linear and nonlinear kernel, random forest, decision tree, adaptive boosting classifier, K-nearest neighbor, and naïve bayes. The results show a clear advantage of using Random Forest with an accuracy of 98% having used 80% of the dataset for training and 20% for testing.","PeriodicalId":249153,"journal":{"name":"2021 3rd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134186211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-23DOI: 10.1109/NILES53778.2021.9600509
S. A. Mohamed, Omar K. Abdelgelil, Osama A. Elhout, Hend M. Aafia, M. Awad, Hossam E. Abd El Munim
This paper proposes a computationally-efficient controller for an AUV which could be implemented using a single-purpose microcontroller. The AUV under study has a complex eight-thruster mechanical configuration. Such system imposes concerns like non-linear behavior, coupled dynamics and parameter uncertainty. An extensive study on vehicle kinematics/dynamics is proposed, followed by formulating a non-linear model for the test vehicle. Dynamic decoupling is applied to break the system into two sub-systems controlled using two independent simple controllers. An LQR controller is used for stabilizing vehicle depth and roll/pitch attitude. A self-tuning PID controller is used for trajectory tracking of surge velocity and yaw attitude. The combined LQR/Adaptive PID control architecture deals very well with noise and uncertainty with minimal computational effort. The controller is verified experimentally using multiple motion scenarios for a test AUV.
{"title":"Design of a highly-efficient embedded controller for AUV stabilization and trajectory tracking using minimal computational resources","authors":"S. A. Mohamed, Omar K. Abdelgelil, Osama A. Elhout, Hend M. Aafia, M. Awad, Hossam E. Abd El Munim","doi":"10.1109/NILES53778.2021.9600509","DOIUrl":"https://doi.org/10.1109/NILES53778.2021.9600509","url":null,"abstract":"This paper proposes a computationally-efficient controller for an AUV which could be implemented using a single-purpose microcontroller. The AUV under study has a complex eight-thruster mechanical configuration. Such system imposes concerns like non-linear behavior, coupled dynamics and parameter uncertainty. An extensive study on vehicle kinematics/dynamics is proposed, followed by formulating a non-linear model for the test vehicle. Dynamic decoupling is applied to break the system into two sub-systems controlled using two independent simple controllers. An LQR controller is used for stabilizing vehicle depth and roll/pitch attitude. A self-tuning PID controller is used for trajectory tracking of surge velocity and yaw attitude. The combined LQR/Adaptive PID control architecture deals very well with noise and uncertainty with minimal computational effort. The controller is verified experimentally using multiple motion scenarios for a test AUV.","PeriodicalId":249153,"journal":{"name":"2021 3rd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114755506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-23DOI: 10.1109/NILES53778.2021.9600501
M. Mousa, F. Amer, Ahmed Saeed, Roaa I. Mubarak
Single junction solar cells have a limitation of absorbing part of the incident spectrum with energy photons lower than the energy gap of the used material, and even the photons with higher energies will generate electron-hole pairs, but the energy difference will be converted into thermalization loss. This problem can be solved by using tandem (multi-junction) solar cells. This work presents a proposed two-terminal Perovskite/Silicon tandem solar cell with 27.69% efficiency. Testing of the tandem cell performance with temperature is also presented.
{"title":"Two-Terminal Perovskite/Silicon Solar Cell: Simulation and Analysis","authors":"M. Mousa, F. Amer, Ahmed Saeed, Roaa I. Mubarak","doi":"10.1109/NILES53778.2021.9600501","DOIUrl":"https://doi.org/10.1109/NILES53778.2021.9600501","url":null,"abstract":"Single junction solar cells have a limitation of absorbing part of the incident spectrum with energy photons lower than the energy gap of the used material, and even the photons with higher energies will generate electron-hole pairs, but the energy difference will be converted into thermalization loss. This problem can be solved by using tandem (multi-junction) solar cells. This work presents a proposed two-terminal Perovskite/Silicon tandem solar cell with 27.69% efficiency. Testing of the tandem cell performance with temperature is also presented.","PeriodicalId":249153,"journal":{"name":"2021 3rd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124556440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-23DOI: 10.1109/NILES53778.2021.9600530
Nabeel Fattah
Medical implant devices (MID) are becoming increasingly used as a means of treating human disorders. Among such MIDs, the visual cortical prosthesis (VCP) is one of the treatments for blind people who want to restore their vision. The visual cortex stimulator (VCS) is a device that requires a reliable power source as well as quick data transfer. Furthermore, the data processing unit must meet specific criteria, such as low power consumption, small size, and efficient processing. One of the most efficient microcontrollers on the market is mentioned in this report. Their characteristics and capabilities are evaluated so that we may select an effective microcontroller that complies with all VCP requirements, including safety and health. The chosen microcontroller (ARM-Cortex M4) was mounted on a single round form printed circuit board (PCB) with a diameter of 30 mm and a thickness of 2.68 mm, together with other necessary components. Furthermore, utilizing low-power Bluetooth, a data transfer speed of 170 Kbps was achieved. Moreover, image decompression time was only 19.55 ms with an overall system power consumption of 80 mW.
{"title":"An Efficient Microcontroller for Visual Cortical Prosthesis","authors":"Nabeel Fattah","doi":"10.1109/NILES53778.2021.9600530","DOIUrl":"https://doi.org/10.1109/NILES53778.2021.9600530","url":null,"abstract":"Medical implant devices (MID) are becoming increasingly used as a means of treating human disorders. Among such MIDs, the visual cortical prosthesis (VCP) is one of the treatments for blind people who want to restore their vision. The visual cortex stimulator (VCS) is a device that requires a reliable power source as well as quick data transfer. Furthermore, the data processing unit must meet specific criteria, such as low power consumption, small size, and efficient processing. One of the most efficient microcontrollers on the market is mentioned in this report. Their characteristics and capabilities are evaluated so that we may select an effective microcontroller that complies with all VCP requirements, including safety and health. The chosen microcontroller (ARM-Cortex M4) was mounted on a single round form printed circuit board (PCB) with a diameter of 30 mm and a thickness of 2.68 mm, together with other necessary components. Furthermore, utilizing low-power Bluetooth, a data transfer speed of 170 Kbps was achieved. Moreover, image decompression time was only 19.55 ms with an overall system power consumption of 80 mW.","PeriodicalId":249153,"journal":{"name":"2021 3rd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124556887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-23DOI: 10.1109/NILES53778.2021.9600546
Abram W. Makram, N. Salem, M. El-Wakad, W. Al-Atabany
In this paper, we propose an effective saliency detection method based on dense and sparse representation in-terms of an optimized background template. Firstly, the input image is divided into compact and uniform super-pixels. Then, the optimized background template is produced by introducing boundary conductivity measurement to improve the dense and sparse representation of the image's super-pixels in terms of the optimized background, where the reconstruction error represents a saliency measure. Based on the optimized template, two saliency maps are generated by dense and sparse representation. Finally, the Bayesian framework used to integrate the two saliency maps to obtain the final one. Experimental results show that the proposed method performs favorably against eight state-of-the-art methods. In addition, the proposed method is shown to be more effective in highlighting the challenging salient objects that touch the image boundary.
{"title":"Robust Background Template for Saliency Detection","authors":"Abram W. Makram, N. Salem, M. El-Wakad, W. Al-Atabany","doi":"10.1109/NILES53778.2021.9600546","DOIUrl":"https://doi.org/10.1109/NILES53778.2021.9600546","url":null,"abstract":"In this paper, we propose an effective saliency detection method based on dense and sparse representation in-terms of an optimized background template. Firstly, the input image is divided into compact and uniform super-pixels. Then, the optimized background template is produced by introducing boundary conductivity measurement to improve the dense and sparse representation of the image's super-pixels in terms of the optimized background, where the reconstruction error represents a saliency measure. Based on the optimized template, two saliency maps are generated by dense and sparse representation. Finally, the Bayesian framework used to integrate the two saliency maps to obtain the final one. Experimental results show that the proposed method performs favorably against eight state-of-the-art methods. In addition, the proposed method is shown to be more effective in highlighting the challenging salient objects that touch the image boundary.","PeriodicalId":249153,"journal":{"name":"2021 3rd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132421029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-23DOI: 10.1109/NILES53778.2021.9600526
Tasneem Wael, Ahmed Hesham, Mohamed Youssef, Omar Adel, Hamis Hesham, M. Darweesh
Text classification has been one of the most common natural language processing (NLP) objectives in recent years. Compared to other languages, this mission with Arabic is relatively restricted and in its early stages, and this combination in the medical application area is rare. This paper builds an Arabic health care assistant, specifically a pediatrician that supports Arabic dialects, especially Egyptian accents. The proposed application is a chatbot based on Artificial Intelligence (AI) models after experimenting with Two Bidirectional Encoder Representations from Transformers (BERT) models, a pre-trained BERT and Logistic regression TF-IDF and Doc2vec. These models were applied to the Arabic dataset with different dialects from different couturiers such as Egypt, Saudi Arabia, and Iraq. The proposed system consists of 4 stages: scrapping and collecting data, then wrangling it, data preprocessing, data extraction, trained models with new data, and connect the model to the database that contains the answers. Experimental tests showed that the BERT model outperformed the others by getting a 95% Accuracy. Logistic regression with Doc2vec was the second best with 71% F-measure and 73% Accuracy.
{"title":"Intelligent Arabic-Based Healthcare Assistant","authors":"Tasneem Wael, Ahmed Hesham, Mohamed Youssef, Omar Adel, Hamis Hesham, M. Darweesh","doi":"10.1109/NILES53778.2021.9600526","DOIUrl":"https://doi.org/10.1109/NILES53778.2021.9600526","url":null,"abstract":"Text classification has been one of the most common natural language processing (NLP) objectives in recent years. Compared to other languages, this mission with Arabic is relatively restricted and in its early stages, and this combination in the medical application area is rare. This paper builds an Arabic health care assistant, specifically a pediatrician that supports Arabic dialects, especially Egyptian accents. The proposed application is a chatbot based on Artificial Intelligence (AI) models after experimenting with Two Bidirectional Encoder Representations from Transformers (BERT) models, a pre-trained BERT and Logistic regression TF-IDF and Doc2vec. These models were applied to the Arabic dataset with different dialects from different couturiers such as Egypt, Saudi Arabia, and Iraq. The proposed system consists of 4 stages: scrapping and collecting data, then wrangling it, data preprocessing, data extraction, trained models with new data, and connect the model to the database that contains the answers. Experimental tests showed that the BERT model outperformed the others by getting a 95% Accuracy. Logistic regression with Doc2vec was the second best with 71% F-measure and 73% Accuracy.","PeriodicalId":249153,"journal":{"name":"2021 3rd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128754538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-23DOI: 10.1109/NILES53778.2021.9600089
Mohamed S. Elsabbagh, A. H. Hassaballa, A. Kamel, Y. Elhalwagy
An adaptive unscented Kalman filter (AUKF) is designed to estimate the roll and pitch angles of rigid body using low-cost inertial sensors. The main challenge is concerned about the tilt orientation estimation in high dynamic environments, where the linear acceleration affects the orientation accuracy. The proposed filter is based on the quaternion technique and an additive function which is used to compensate the influence of accelerometer corrections during motions. The algorithm is implemented on a STM32F407 ARM Cortex-M4 series microcontrollers and fused three-axis accelerometer and, three single-axis gyroscopes triads based on micro-electromechanical system (MEMS) technology. The experimental and field tests results analysis showed an outstanding real-time navigation performance when compared with the traditional KF and other commercial expensive systems.
设计了一种自适应无气味卡尔曼滤波(AUKF)，利用低成本惯性传感器估计刚体的横滚角和俯仰角。主要的挑战是在高动态环境下的倾斜方向估计，其中线性加速度会影响方向精度。该滤波器基于四元数技术和用于补偿运动过程中加速度计修正影响的加性函数。该算法在STM32F407 ARM Cortex-M4系列微控制器和基于微机电系统(MEMS)技术的融合三轴加速度计和三个单轴陀螺仪三联上实现。实验和现场测试结果分析表明，与传统KF和其他昂贵的商用系统相比，该系统具有出色的实时导航性能。
{"title":"Precise Orientation Estimation Based on Nonlinear Modeling and Quaternion Transformations for Low Cost Navigation Systems","authors":"Mohamed S. Elsabbagh, A. H. Hassaballa, A. Kamel, Y. Elhalwagy","doi":"10.1109/NILES53778.2021.9600089","DOIUrl":"https://doi.org/10.1109/NILES53778.2021.9600089","url":null,"abstract":"An adaptive unscented Kalman filter (AUKF) is designed to estimate the roll and pitch angles of rigid body using low-cost inertial sensors. The main challenge is concerned about the tilt orientation estimation in high dynamic environments, where the linear acceleration affects the orientation accuracy. The proposed filter is based on the quaternion technique and an additive function which is used to compensate the influence of accelerometer corrections during motions. The algorithm is implemented on a STM32F407 ARM Cortex-M4 series microcontrollers and fused three-axis accelerometer and, three single-axis gyroscopes triads based on micro-electromechanical system (MEMS) technology. The experimental and field tests results analysis showed an outstanding real-time navigation performance when compared with the traditional KF and other commercial expensive systems.","PeriodicalId":249153,"journal":{"name":"2021 3rd Novel Intelligent and Leading Emerging Sciences Conference (NILES)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125452768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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